:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Gupta, Shivam, Tarushi, Wangzes, Tsering, Jain, Shweta
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2407.04302
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Fairness and Privacy Guarantees in Federated Contextual Bandits
by: Solanki, Sambhav, et al.
Published: (2024)

Towards Fairness in Provably Communication-Efficient Federated Recommender Systems
by: Kaur, Kirandeep, et al.
Published: (2024)

Harnessing Data from Clustered LQR Systems: Personalized and Collaborative Policy Optimization
by: Kanakeri, Vinay, et al.
Published: (2025)

Fairness of Exposure in Online Restless Multi-armed Bandits
by: Sood, Archit, et al.
Published: (2024)

Meritocratic Fairness in Budgeted Combinatorial Multi-armed Bandits via Shapley Values
by: Sharma, Shradha, et al.
Published: (2026)

Diversity Augmentation of Dynamic User Preference Data for Boosting Personalized Text Summarizers
by: Chatterjee, Parthiv, et al.
Published: (2025)

Federated Learning with Uncertainty and Personalization via Efficient Second-order Optimization
by: Pal, Shivam, et al.
Published: (2024)

Bridging Gaps: Federated Multi-View Clustering in Heterogeneous Hybrid Views
by: Chen, Xinyue, et al.
Published: (2024)

Federated Clustering: An Unsupervised Cluster-Wise Training for Decentralized Data Distributions
by: Nardi, Mirko, et al.
Published: (2024)

Bridging the Semantic Gap for Categorical Data Clustering via Large Language Models
by: Yang, Zihua, et al.
Published: (2026)

One-Shot Federated Clustering of Non-Independent Completely Distributed Data
by: Zhang, Yiqun, et al.
Published: (2026)

Alignment through Meta-Weighted Online Sampling: Bridging the Gap between Data Generation and Preference Optimization
by: Yang, Junming, et al.
Published: (2025)

MITA: Bridging the Gap between Model and Data for Test-time Adaptation
by: Yuan, Yige, et al.
Published: (2024)

Loss Gap Parity for Fairness in Heterogeneous Federated Learning
by: Erraji, Brahim, et al.
Published: (2026)

Mind the Data Gap: Bridging LLMs to Enterprise Data Integration
by: Kayali, Moe, et al.
Published: (2024)

HARMONY: Bridging the Personalization-Generalization Gap by Mitigating Representation Skew in Heterogeneous Split Federated Learning
by: Youn, Jiseok, et al.
Published: (2026)

Friends in Unexpected Places: Enhancing Local Fairness in Federated Learning through Clustering
by: Yang, Yifan, et al.
Published: (2024)

Cooperative SGD with Dynamic Mixing Matrices
by: Sarkar, Soumya, et al.
Published: (2025)

Simultaneously Achieving Group Exposure Fairness and Within-Group Meritocracy in Stochastic Bandits
by: Pokhriyal, Subham, et al.
Published: (2024)

Autoregressive Ranking: Bridging the Gap Between Dual and Cross Encoders
by: Rozonoyer, Benjamin, et al.
Published: (2026)

Flexible Clustered Federated Learning for Client-Level Data Distribution Shift
by: Duan, Moming, et al.
Published: (2021)

FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust Clustering
by: Guo, Yongxin, et al.
Published: (2023)

Bridging Data Gaps in Healthcare: A Scoping Review of Transfer Learning in Biomedical Data Analysis
by: Li, Siqi, et al.
Published: (2024)

Federated Unlearning in the Wild: Rethinking Fairness and Data Discrepancy
by: Huang, ZiHeng, et al.
Published: (2025)

PROPER Agents: Proactivity Driven Personalized Agents for Advancing Knowledge Gap Navigation
by: Kaur, Kirandeep, et al.
Published: (2026)

Insights into the Unknown: Federated Data Diversity Analysis on Molecular Data
by: Bujotzek, Markus, et al.
Published: (2025)

D3FL: Data Distribution and Detrending for Robust Federated Learning in Non-linear Time-series Data
by: Marisetty, Harsha Varun, et al.
Published: (2025)

Counterfactual Fairness through Transforming Data Orthogonal to Bias
by: Chen, Shuyi, et al.
Published: (2024)

Bridging Jensen Gap for Max-Min Group Fairness Optimization in Recommendation
by: Xu, Chen, et al.
Published: (2025)

CLoVE: Personalized Federated Learning through Clustering of Loss Vector Embeddings
by: Bhatia, Randeep, et al.
Published: (2025)

On ADMM in Heterogeneous Federated Learning: Personalization, Robustness, and Fairness
by: Zhu, Shengkun, et al.
Published: (2024)

Bridging the Gap: Protocol Towards Fair and Consistent Affect Analysis
by: Hu, Guanyu, et al.
Published: (2024)

CCFC: Bridging Federated Clustering and Contrastive Learning
by: Liu, Jing, et al.
Published: (2024)

The Diversity Bonus: Learning from Dissimilar Distributed Clients in Personalized Federated Learning
by: Wu, Xinghao, et al.
Published: (2024)

Factor-Assisted Federated Learning for Personalized Optimization with Heterogeneous Data
by: Wang, Feifei, et al.
Published: (2023)

A Stochastic Optimization Framework for Private and Fair Learning From Decentralized Data
by: Gupta, Devansh, et al.
Published: (2024)

Bridging the Physics-Data Gap with FNO-Guided Conditional Flow Matching: Designing Inductive Bias through Hierarchical Physical Constraints
by: Okita, Tsuyoshi
Published: (2025)

Building Bridges between Regression, Clustering, and Classification
by: Stewart, Lawrence, et al.
Published: (2025)

FairContrast: Enhancing Fairness through Contrastive learning and Customized Augmenting Methods on Tabular Data
by: Tayebi, Aida, et al.
Published: (2025)

Bridging the Generalisation Gap: Synthetic Data Generation for Multi-Site Clinical Model Validation
by: Segal, Bradley, et al.
Published: (2025)